Triple
T4519904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanti Parva |
E103239
|
entity |
| Predicate | hasApproximateChapterCount |
P2946
|
FINISHED |
| Object | around 300 or more chapters in critical editions |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: around 300 or more chapters in critical editions | Statement: [Shanti Parva, hasApproximateChapterCount, around 300 or more chapters in critical editions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateChapterCount Context triple: [Shanti Parva, hasApproximateChapterCount, around 300 or more chapters in critical editions]
-
A.
numberOfChapters
chosen
Indicates the total count of chapters associated with a given entity.
-
B.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
C.
hasPageCountApprox
Indicates that an entity is associated with an approximate or estimated number of pages, rather than an exact page count.
-
D.
approximateNumberOfVerses
Indicates an estimated or approximate count of verses associated with an entity.
-
E.
hasLocalChaptersIn
Indicates that an organization maintains one or more local chapters or branches within a specified geographic area or location.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd43dba59881908cf59b31df8c7ae1 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5747e90c81908fa112ecace699a9 |
completed | March 20, 2026, 2:18 p.m. |
| PD | Predicate disambiguation | batch_69bd521abea48190b3e758a1f98dd55e |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:02 p.m.